MSCAGR (Agricultural Economics) / Department of Agricultural Economics / Agricultural cooperatives are important tools for enhancing the living standards of farmers in rural areas. It is therefore very clear that cooperatives are for the benefit of the farmers. However, the development of cooperatives in the study area is not at a desired level yet; hence, it is necessary to determine the farmers’ attitudes towards forming cooperatives. The research was carried out in Makhado Local Municipality, Vhembe District in Limpopo Province. Three irrigation schemes were selected for the study, which consist of a total of 215 smallholder farmers. However, only 152 smallholder irrigation farmers were used for the study. The mixed research design method was used for this study. The sampling technique used is purposive sampling. Data was collected through a structured questionnaire. Interviews were also made using key informants (Extension Office). The Statistical Package for Social Sciences (SPSS) was used to analyse the data. Cross tabulations and the logistic regression were used to analyse the data. The study revealed that the socioeconomic characteristics smallholder irrigation farmers has an impact on their willingness to form cooperatives. The study also revealed that the attitudes of farmers have an impact on their willingness to form cooperatives. The study further revealed that the constraints such as trainings, hired service providers, costs of inputs, access to agricultural information, access to adequate land and access to markets have an impact on their willingness to form cooperatives. The study recommended that strategies can be implemented on how cooperatives can be formed and enhance their success. The study also shows that future research can be done in youth participation in agriculture and cooperatives, cooperatives partnering with agricultural companies and other organisations. / NRF

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Logic regression (LR) is a methodology to identify logic combinations of binary predictors in the form of intersections (and), unions (or) and negations (not) that are linearly associated with an outcome variable. Logic regression uses the predictors as inputs and enables us to identify important logic combinations of independent variables using a computationally efficient tree-based stochastic search algorithm, unlike the classical regression models, which only consider pre-determined conventional interactions (the “and” rules). In the thesis, we focused on LR with a binary outcome in a logistic regression framework. Simulation studies were conducted to examine the performance of LR under the assumption of independent and correlated observations, respectively, for various characteristics of the data sets and LR search parameters. We found that the proportion of times that LR selected the correct logic rule was usually low when the signal and/or prevalence of the true logic rule were relatively low. The method performed satisfactorily under easy learning conditions such as high signal, simple logic rules and/or small numbers of predictors. Given the simulation characteristics and correlation structures tested, we found some but not significant difference in performance when LR was applied to dependent observations compared to the independent case. In addition to simulation studies, an advanced application method was proposed to integrate LR and resampling methods in order to enhance LR performance. The proposed method was illustrated using two simulated data sets as well as a data set from a real-life situation. The proposed method showed some evidence of being effective in discerning the correct logic rule, even for unfavorable learning conditions.

Due to recent advances in technology that facilitate acquisition of multi-parameter defined phenotypes, new opportunities have arisen for predicting patient outcomes based on individual specific cell subset changes. The data resulting from these trials can be a challenge to analyze, as predictors may be highly correlated with each other or related to outcome within levels of other predictor variables. As a result, applying traditional methods like simple linear models and univariate approaches such as odds ratios may be insufficient. In this dissertation, we describe potential solutions including tree-based methods, ridge regression, mixed modeling, and a new estimator called a mixed ridge estimator with expectation-maximization (EM) algorithm. Data examples are provided. In particular, flow cytometry is a method of measuring a large number of particle counts at once by suspending them in a fluid and shining a beam of light onto the fluid. This is specifically relevant in the context of studying human immunodeficiency virus (HIV), where there exists a great potential to draw from the rich array of data on host cell-mediated response to infection and drug exposures, to inform and discover patient level determinants of disease progression and/or response to anti-retroviral therapy (ART). The data sets collected are often high dimensional with correlated columns, which can be challenging to analyze. We demonstrate the application and comparative interpretations of three tree-based algorithms for the analysis of data arising from flow cytometry in the first chapter of this manuscript. Specifically, we consider the question of what best predicts CD4 T-cell recovery in HIV-1 infected persons starting antiretroviral therapy with CD4 count between 200-350 cell/μl. The tree-based approaches, namely, classification and regression trees (CART), random forests (RF) and logic regression (LR), were designed specifically to uncover complex structure in high dimensional data settings. While contingency table analysis and RFs provide information on the importance of each potential predictor variable, CART and LR offer additional insight into the combinations of variables that together are predictive of the outcome. Specifically, application of tree-based methods to our data suggest that a combination of baseline immune activation states, with emphasis on CD8 T cell activation, may be a better predictor than any single T cell/innate cell subset analyzed. In the following chapter, tree-based methods are compared to each other via a simulation study. Each has its merits in particular circumstances; for example, RF is able to identify the order of importance of predictors regardless of whether there is a tree-like structure. It is able to adjust for correlation among predictors by using a machine learning algorithm, analyzing subsets of predictors and subjects over a number of iterations. CART is useful when variables are predictive of outcome within levels of other variables, and is able to find the most parsimonious model using pruning. LR also identifies structure within the set of predictor variables, and nicely illustrates relationship among variables. However, due to the vast number of combinations of predictor variables that would need to be analyzed in order to find the single best LR tree, an algorithm is used that only searches a subset of potential combinations of predictors. Therefore, results may be different each time the algorithm is used on the same data set. Next we use a regression approach to analyzing data with correlated predictors. Ridge regression is a method of accounting for correlated data by adding a shrinkage component to the estimators for a linear model. We perform a simulation study to compare ridge regression to linear regression over various correlation coefficients and find that ridge regression outperforms linear regression as correlation increases. To account for collinearity among the predictors along with longitudinal data, a new estimator that combines the applicability of ridge regression and mixed models using an EM algorithm is developed and compared to the mixed model. We find from a simulation study comparing our mixed ridge (MR) approach with a traditional mixed model that our new mixed ridge estimator is able to handle collinearity of predictor variables better than the mixed model, while accounting for random within-subject effects that regular ridge regression does not take into account. As correlation among predictors increases, power decreases more quickly for the mixed model than MR. Additionally, type I error rate is not significantly elevated when the MR approach is taken. The MR estimator gives us new insight into flow cytometry data and other data sets with correlated predictor variables that our tree-based methods could not give us. These methods all provide unique insight into our data that more traditional methods of analysis do not offer.

PhD (Agricultural Economics) / Department of Agricultural Economics and Agribusiness / Climate change is a global phenomenon that has been of great concern and its tackle is of outmost importance for food security among other things. In response to climate change adaptation, the study intended to determine awareness of climate change, its critical determinants and impacts among farmers, particularly emerging farmers. The study also investigated socio-economic characteristics of farmers that play a vital role in selection of various adaptive strategies, furthermore, institutional factors that contributed in emerging farmers’ decision to either adapt or not to climate change were also investigated. The main aim of the study was to develop a model that could be used in future to enhance adaptation to climate change through various identified adaptive strategies in Limpopo province of South Africa. The study was conducted in five districts of Limpopo province, namely: Capricorn, Mopani, Sekhukhune, Vhembe and Waterberg. The study made use of structured questionnaire to collect data from 206 emerging farmers. A two-stage cluster sampling technique was employed to select participants of the study. Statistical Package for the Social Sciences (SPSS; version 25, 2017) was used to analyse the data; cross-tabulation, multinomial and binary logistic models were used for analysis. Preliminary descriptive statistics results from cross-tabulation indicated that farmers were aware of climate change; had noted various critical determinants of climate change and were aware of impacts of climate change during production seasons between 2014 and 2018. Using Multinomial Logit model, further analysis indicated that there are socio-economic characteristics that significantly influenced selection of various adaptive strategies among farmers. Variables that significantly influenced selection of various adaptive strategies were household size, farming experience, formal education, occupation, gender and monthly on-farm income. The study also discovered that institutional factors such as accessing different kinds of extension services, securing source of support and accessing climate change information such as weather forecast, positively and significantly influence farmers’ decision to adapt to climate change. Recommendations of the study were that there should be capacity building in a form of training programmes that promote climate change awareness as farmers need to be capacitated to enable them to take strategic decisions on a daily basis. Furthermore, it was also recommended that training of farmers should target illiterate farmers and farmer without off-farm occupation and specific needs of farmers should be taken into consideration when initiating adaptation initiatives as adaptation to climate change is best monitored at farm level. The study also recommended that various stakeholders such as community of practice, climatologists, and agro-meteorologists should provide various support to emerging farmers to improve farmers’ resilience towards climate change through adaptation. / NRF

Les nouvelles technologies développées ces dernières années dans le domaine de la génétique ont permis de générer des bases de données de très grande dimension, en particulier de Single Nucleotide Polymorphisms (SNPs), ces bases étant souvent caractérisées par un nombre de variables largement supérieur au nombre d'individus. L'objectif de ce travail a été de développer des méthodes statistiques adaptées à ces jeux de données de grande dimension et permettant de sélectionner les variables les plus pertinentes au regard du problème biologique considéré. Dans la première partie de ce travail, un état de l'art présente différentes méthodes de sélection de variables non supervisées et supervisées pour 2 blocs de variables et plus. Dans la deuxième partie, deux nouvelles méthodes de sélection de variables non supervisées de type "sparse" sont proposées : la Group Sparse Principal Component Analysis (GSPCA) et l'Analyse des Correspondances Multiples sparse (ACM sparse). Vues comme des problèmes de régression avec une pénalisation group LASSO elles conduisent à la sélection de blocs de variables quantitatives et qualitatives, respectivement. La troisième partie est consacrée aux interactions entre SNPs et dans ce cadre, une méthode spécifique de détection d'interactions, la régression logique, est présentée. Enfin, la quatrième partie présente une application de ces méthodes sur un jeu de données réelles de SNPs afin d'étudier l'influence possible du polymorphisme génétique sur l'expression du vieillissement cutané au niveau du visage chez des femmes adultes. Les méthodes développées ont donné des résultats prometteurs répondant aux attentes des biologistes, et qui offrent de nouvelles perspectives de recherches intéressantes / New technologies developed recently in the field of genetic have generated high-dimensional databases, especially SNPs databases. These databases are often characterized by a number of variables much larger than the number of individuals. The goal of this dissertation was to develop appropriate statistical methods to analyse high-dimensional data, and to select the most biologically relevant variables. In the first part, I present the state of the art that describes unsupervised and supervised variables selection methods for two or more blocks of variables. In the second part, I present two new unsupervised "sparse" methods: Group Sparse Principal Component Analysis (GSPCA) and Sparse Multiple Correspondence Analysis (Sparse MCA). Considered as regression problems with a group LASSO penalization, these methods lead to select blocks of quantitative and qualitative variables, respectively. The third part is devoted to interactions between SNPs. A method employed to identify these interactions is presented: the logic regression. Finally, the last part presents an application of these methods on a real SNPs dataset to study the possible influence of genetic polymorphism on facial skin aging in adult women. The methods developed gave relevant results that confirmed the biologist's expectations and that offered new research perspectives.